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10 PYTHON LIBRARIES FOR DATA ANALYSTS
1️⃣ Pandas
✅ A library for data manipulation and analysis that provides powerful data structures for working with structured data, such as tables and time series.
➡ Example: Using Pandas to read in and clean a CSV file of customer data, then calculating summary statistics such as mean, median, and mode for key metrics.
2️⃣ NumPy
✅ A library for numerical computing that provides fast and efficient operations for working with arrays and matrices.
➡ Example: Using NumPy to perform linear algebra operations on a matrix of customer purchase data to calculate customer lifetime value.
3️⃣ Matplotlib
✅ A library for creating visualizations such as line charts, scatter plots, and bar charts.
➡ Example: Using Matplotlib to create a histogram of customer purchase frequency to identify the most common purchase amounts.
4️⃣ Seaborn
✅ A library for creating statistical visualizations such as heatmaps, box plots, and violin plots.
➡ Example: Using Seaborn to create a scatter plot of customer purchase frequency versus purchase amount to identify patterns in customer behavior.
5️⃣ Scikit-learn
✅ A library for machine learning that provides tools for classification, regression, clustering, and more.
➡ Example: Using Scikit-learn to build a predictive model that identifies which customers are most likely to churn.
6️⃣ Statsmodels
✅ A library for statistical analysis that provides tools for hypothesis testing, regression analysis, and time series analysis.
➡ Example: Using Statsmodels to perform a regression analysis of customer purchase behavior to identify the most important factors that influence purchase frequency.
7️⃣ BeautifulSoup
✅ A library for web scraping that provides tools for parsing HTML and XML documents.
➡ Example: Using BeautifulSoup to scrape data from a website and extract key metrics such as pageviews and bounce rate.
8️⃣ NetworkX
✅ A library for analyzing and visualizing networks, such as social networks or transportation networks.
➡ Example: Using NetworkX to analyze the connections between customers and identify the most influential customers in a social network.
9️⃣ PySpark
✅ A library for working with big data using the Apache Spark framework.
➡ Example: Using PySpark to perform distributed computing on a large dataset of customer transactions to calculate summary statistics.
🔟 Requests
✅ A library for making HTTP requests to web servers.
➡ Example: Using Requests to retrieve data from an API and integrate it with internal customer data for analysis.